The method proposed below (by Andrew Chisholm) worked for me. And is a much better approach then the one I initially took. Additionally turns out that using that approach allows me to train (using the GPU) while commuting. The GPU is throttled, so it’s slower then when in the office. However still much quicker then when using the CPU.
Okay my first time on this forum, but as Jeremy advised to use it a lot here goes:
I’ve got a laptop containing a 1050 with primary boot being windows and secondary boot linux (arch). I started the course on the arch boot, but I ran into a major issue while in commute when i’m dependent on the battery. As soon as a training session was started
learn.fit_one_cycle(1), the laptop would power down. This is, I believe, due to the high power consumption of the GPU. Thinking better of trying to implement some form of gpu throttling on linux as training while traveling doesn’t really make sense anyway. I still though I’d give it a go on windows 10. Scouting this forum and others I failed to find one that seemed to work for me. The post by Jeremy on it I believe was meant for version 0.7, and gave me an error on
ImportError: cannot import name ‘as_tensor’.
However the following, very easy, approach worked for me:
- Install Ubuntu
- While in ubuntu terminal:
pip install fastai.
That’s it. No further needs for simlinks or anything. I do have to mention however that when I had previously installed Ubuntu and am not sure what has been installed in the mean time, I do remember the following packages: nvidia-driver-390.
Having said that using any of the cloud services will probably help a lot during the course. A single cycle for me during lesson1 from the 2019 course took roughly 23 minutes versus <30 seconds in the lecturers, while using external power supply. Also at the end I had a loss of ~0.071, opposed to ~0.061 in the lecturers. I’m not sure where this difference came from so if someone knows? Reducing batch size to 16 lowered the time to ~ 17 minutes per cycle.
When switching to battery the estimated time increased to slightly over an hour, which I didn’t allow to finish. Also because I’m not sure my battery would actually hold out that long. Still hopes this may help some others.